Impact of the COVID-19 pandemic on dengue in Brazil: Interrupted time series analysis of changes in surveillance and transmission

by Kirstin Oliveira Roster, Tiago Martinelli, Colm Connaughton, Mauricio Santillana, Francisco A. Rodrigues

Measures to curb the spread of SARS-CoV-2 impacted not only COVID-19 dynamics, but also other infectious diseases, such as dengue in Brazil. The COVID-19 pandemic disrupted not only transmission dynamics due to changes in mobility patterns, but also several aspects of surveillance, such as care seeking behavior and clinical capacity. However, we lack a clear understanding of the overall impact on dengue in different parts of Brazil and the contribution of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in each Brazilian state from March to April 2020 using an interrupted time series design with forecasts from machine learning models. We then decomposed the gap into the contributions of pandemic-induced changes in disease surveillance and transmission dynamics, using proxies for care availability and care seeking behavior. Of 25 states in the analysis, 19 reported fewer dengue cases than predicted and the gap between expected and observed cases was largely explained by excess under-reporting, as illustrated in several states by a reduction in observed cases below expected levels in early March 2020 in several states. A notable exception is the experience in the Southern states, which reported unusually large dengue outbreaks in 2020. These estimates of dengue case counts adjusted for under-reporting help mitigate some of the data gaps from 2020. Reliable estimates of changes in the disease burden are critical for anticipating future outbreaks.